def test_leq(self):
for _, data in self.__database:
algo = essentia.standard.Leq()
generated = temporal.leq(data)
reference = algo.compute(data.astype(np.float32))
error = utility.get_change(generated, reference)
self.assertTrue(error < 1)
def test_duration(self):
for fs, data in self.__database:
algo = essentia.standard.Duration(sampleRate=fs)
generated = temporal.duration(data, fs)
reference = algo.compute(data.astype(np.float32))
error = utility.get_change(generated, reference)
self.assertTrue(error < 1)
def test_weighted_centroid(self):
for fs, data in self.__database:
ind = (np.arange(1, len(data) + 1)) * (fs / (2.0 * len(data)))
generated = statistics.weighted_centroid(data, ind)
centroid, _ = extractor.stSpectralCentroidAndSpread(data, fs)
reference = centroid * (fs / 2.0) # de-normalize
error = utility.get_change(generated, reference)
self.assertTrue(error < 1)
def test_rms(self):
for _, data in self.__database:
data = np.iinfo(np.uint8).max * data
algo = essentia.standard.RMS()
generated = temporal.rms(data)
reference = algo.compute(data.astype(np.float32))
error = utility.get_change(generated, reference)
self.assertTrue(error < 1)
def test_effective_duration(self):
for fs, data in self.__database:
threshold = random.uniform(0.0, 1.0)
algo = essentia.standard.EffectiveDuration(
sampleRate=fs, thresholdRatio=threshold)
generated = temporal.effective_duration(data, fs, threshold)
reference = algo.compute(data.astype(np.float32))
error = utility.get_change(generated, reference)
self.assertTrue(error < 1)
def test_spectral_centroid(self):
for fs, data in self.__database:
_, data = scipy.signal.periodogram(data, fs)
data = (data + utility.epsilon) * 100
ind = (np.arange(1, len(data) + 1)) * (fs / (2.0 * len(data)))
generated = spectral.spectral_centroid(data, ind)
centroid, _ = extractor.stSpectralCentroidAndSpread(data, fs)
reference = centroid * (fs / 2.0) # de-normalize
error = utility.get_change(generated, reference)
self.assertTrue(error < 1)
